https://github.com/BRML/climin

Optimizers for machine learning
https://github.com/BRML/climin

Last synced: 24 days ago
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Optimizers for machine learning

• Host: GitHub
• URL: https://github.com/BRML/climin
• Owner: BRML
• License: other
• Created: 2011-12-02T13:46:03.000Z (over 12 years ago)
• Default Branch: master
• Last Pushed: 2023-09-07T15:49:35.000Z (9 months ago)
• Last Synced: 2024-03-26T18:25:56.562Z (2 months ago)
• Language: Python
• Homepage:
• Size: 796 KB
• Stars: 179
• Watchers: 29
• Forks: 65
• Open Issues: 19
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• AI - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learnings - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning-library - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-master - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others (Python / General-Purpose Machine Learning)
• awesome-machine-learning - climin - Optimization library focused on machine learning, pythonic implementations of gradient descent, LBFGS, rmsprop, adadelta and others. (Python / General-Purpose Machine Learning)
• awesome-machine-learning-cn - 官网

README

        
climin

------

climin is a Python package for optimization, heavily biased to machine learning

scenarios distributed under the BSD 3-clause license. It works on top of numpy

and (partially) gnumpy.

The project was started in winter 2011 by Christian Osendorfer and Justin Bayer.

Since then, Sarah Diot-Girard, Thomas Rueckstiess and Sebastian Urban have 

contributed. If you use climin in your (academic) work, please cite as 

(tech report is in preparation):

> J. Bayer and C. Osendorfer and S. Diot-Girard and T. Rückstiess and Sebastian Urban. climin - A pythonic framework for gradient-based function optimization. TUM Tech Report. 2016. http://github.com/BRML/climin

Important links

---------------

 - Official repository of source: http://github.com/BRML/climin

 - Documentation: http://climin.readthedocs.org

 - Mailing list: [email protected] (archive: http://librelist.com/browser/climin/)

Dependencies

------------

The software is tested under Python 2.7 with numpy 1.10.4, scipy 0.17. The tests

are run with nosetests.

Installation

------------

Use git to clone the official repository; then run `pip install --user -e .`

in the clone to intall in your local user space.

Testing

-------

From the download directory run ``nosetests tests/``.